Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
Multi-way set enumeration in real-valued tensors
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Networks, communities and kronecker products
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Trust relationship prediction using online product review data
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Hydra: a hybrid recommender system [cross-linked rating and content information]
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Tensor Decompositions and Applications
SIAM Review
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Network growth and the spectral evolution model
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Collective sampling and analysis of high order tensors for chatroom communications
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Pattern recognition and classification for multivariate time series
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Pattern recognition in multivariate time series: dissertation proposal
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Hi-index | 0.00 |
Within the last few years a lot of research has been done on large social and information networks. One of the principal challenges concerning complex networks is link prediction. Most link prediction algorithms are based on the underlying network structure in terms of traditional graph theory. In order to design efficient algorithms for large scale networks, researchers increasingly adapt methods from advanced matrix and tensor computations. This paper proposes a novel approach of link prediction for complex networks by means of multi-way tensors. In addition to structural data we furthermore consider temporal evolution of a network. Our approach applies the canonical Parafac decomposition to reduce tensor dimensionality and to retrieve latent trends. For the development and evaluation of our proposed link prediction algorithm we employed various popular datasets of online social networks like Facebook and Wikipedia. Our results show significant improvements for evolutionary networks in terms of prediction accuracy measured through mean average precision.